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If you are into machine learning and data science, you must've come across the online community called Kaggle. Kaggle is the world's largest data science network, promoting an array of courses, books, and tutorials to educate students, professionals, and even experts. Kaggle is seen as an amazing place for people starting their careers in machine learning and data science. Kaggle competitions help students or aspiring candidates to learn and know more about the technological aspect of things, and sometimes, they even lead to big money prizes. This article features the top 10 Kaggle competitions in data science students can enroll in.
Volatility is one of the most prominent terms you'll hear on any trading floor – and for good reason. In financial markets, volatility captures the amount of fluctuation in prices. High volatility is associated with periods of market turbulence and large price swings, while low volatility describes more calm and quiet markets. For trading firms like Optiver, accurately predicting volatility is essential for the trading of options, whose price is directly related to the volatility of the underlying product.
As a leading global electronic market maker, Optiver is dedicated to continuously improving financial markets, creating better access and prices for options, ETFs, cash equities, bonds, and foreign currencies on numerous exchanges around the world. Optiver's teams have spent countless hours building sophisticated models that predict volatility and continuously generate fairer option prices for end investors. However, an industry-leading pricing algorithm can never stop evolving, and there is no better place than Kaggle to help Optiver take its model to the next level.
Kaggle competitions are incredibly fun and rewarding, but they can also be intimidating for people who are relatively new in their data science journey. In the past, we've launched many Playground competitions that are more approachable than our Featured competitions and thus, more beginner-friendly.
The goal of these competitions is to provide a fun and approachable-for-anyone tabular dataset to model. These competitions are a great choice for people looking for something in between the Titanic Getting Started competition and the Featured competitions. If you're an established competitions master or grandmaster, these probably won't be much of a challenge for you; thus, we encourage you to avoid saturating the leaderboard.
When you look for nearby restaurants or plan an errand in an unknown area, you expect relevant, accurate information. Maintaining quality data worldwide is a challenge, and one with implications beyond navigation. Businesses make decisions on new sites for market expansion, analyze the competitive landscape, and show relevant ads informed by location data. For these, and many other uses, reliable data is critical.
Large-scale datasets on commercial points-of-interest (POI) can be rich with real-world information. To maintain the highest level of accuracy, the data must be matched and de-duplicated with timely updates from multiple sources. De-duplication involves many challenges, as the raw data can contain noise, unstructured information, and incomplete or inaccurate attributes. A combination of machine-learning algorithms and rigorous human validation methods is optimal to de-dupe datasets.
As mentioned above, Kaggle works to strengthen the career of starters by giving them easy-to-hard competitions. Titanic ML competition is a starter test that helps aspirants to dive into many upcoming machine learning competitions and familiarize themselves with how Kaggle works. The competition is purely for beginners who are starting a career in machine learning. It puts forth a simple task to use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. The Kaggle competition explains the situation to participants and expects them to solve it using machine learning technology.
This is not a beginners' course, but it still helps machine learning students or aspirants to enhance their knowledge base. To take up the course, the candidate should have some experience with R or Python and machine learning basics. Data science students who have a keen interest in machine learning and have completed basic courses on ML can participate in this competition. House Prices competition challenges participants to predict sales prices and practice feature engineering, RFs, and gradient boosting. They have provided 79 explanatory variables, describing every aspect of residential homes in Ames, Iowa, and demanded participants predict the final price of each home.
People who are well-versed in technology always try to learn every disruptive trend that emerges. Even machine learning experts can try their hands-on computer vision. That is what this competition promotes. If the participant has experience with R or Python and machine learning basics but is new to computer vision, then the competition gives them a chance to enhance their computer vision knowledge. It introduces techniques like neural networks using a classic dataset including pre-extracted features. The competition demands participants identify digits from a dataset of tens of thousands of handwritten images.
This competition introduces data scientists to a less-explored concept called natural language processing (NLP). Even without much computing knowledge, participants can take up the competition and use Kaggle's free, no-set-up, Jupyter Notebooks environment known as Kaggle Notebooks. The competition demands participants predict if the tweets are about real disasters or not. Twitter is increasingly becoming an important mode of communication. Even governments are using it to announce their stance and new initiatives. On the other hand, it is used as a rapid mode of communication to report on disasters. Therefore, Kaggle challenges the participants to find whether the tweet is about a natural disaster or not with the help of NLP.
Connect X is a beta-version of a brand-new type of machine learning completion called 'Simulations.' Instead of going against an evaluation metric, Simulation completion demands participants to compete against a set of rules. Participants should create a Python submission file that can play against a computer or another user. By using technology, participants should align their checkers in a row horizontally, vertically, or diagonally on the game board before their opponents.
By taking up the course, participants get to experience a new concept called Tensor Processing Units (TPUs). Designed by Google to process large image databases, TPUs are powerful hardware accelerators specialized in deep learning tasks. In this competition, participants are asked to build a machine learning model that identifies the types of flowers in a dataset of images.
Every artist has their style of giving a personal touch to their art. The competition uses that note to challenge participants on identifying the artist based on their unique style and create a similar art with the help of technology. To do this, the participants can get the help of computer vision. Computer vision has advanced tremendously in recent years and GANs are now capable of mimicking objects in a very convincing way. They can use a generator model and a discriminator model to generate images in the style of Monet.
